1. Field of the Invention
The present invention relates to a hybrid apparatus for recognizing answer type, and more particularly, to a hybrid model and method for recognizing answer types in order to recognize a Korean answer type.
2. Description of the Related Art
Generally, a named entity recognition scheme extracts core information from a text and it is a necessary function to be included in a question and answer system or a text mining system. Particularly, a named entity recognizer is a major module of a FLACON's question and answer system or an IBM's question and answer system for recognizing an answer type. That is, the named entity recognizer recognizes the answer type and generates correct answer in the question and answer system. Although the answer type is not exactly matched with a recognized named entity, the named entity recognition scheme can be used for recognizing the answer type. The answer type for the question and answer system requires more categories than the named entity recognition.
The named entity recognition scheme has been technologies. Statistical approaching methods are generally used as the named entity recognition scheme and among the statically approaching methods, a hidden markov model (HMM) or a maximum-entropy (ME) have been widely used. In the HMM or the ME, a named entity type is allocated to target vocabulary based on a history information. For English, a Bikel shows 90% of F-measure by using a simple vocabulary feature and a Zhou introduces a HMM based named entity recognition method using complex features and it has 93.4% of F-measure.
A Srihari's model is one of representative model for a hybrid method as a sub categorized named entity recognition. In the Srihari's model, a named entity is recognized by using the ME model, the HMM model and a grammar model generated by manual work. And the Srihari's model also introduces the sub categorized recognition method by using the ME model. However, a pattern rule is constructed based on manual work and it has been introduced a sub categorized recognition scheme using an external dictionary information instead of using a named entity tagged corpus.
A conventional hybrid approach has been used for recognizing a Korean named entity by using a ME model, a neural network and a pattern selection rule. The hybrid method shows 84.09% as F-measure when the named entity type is person, place and organization. Considering a performance of Korean named entity recognition it is lower than the performance of English named entity recognition. It is because there is no capital letter feature of a proper noun in Korean. The English includes the capital letter feature in a proper noun. In the view of no capital letter feature in Korean, the conventional research may be adaptable. However, the conventional hybrid method is a test based study using simple named entities as target entities and there is no method introduced for recognizing mess amount of answer types.